International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Water scarcity, unpredictable weather patterns, and inefficient agricultural water management continue to pose significant challenges to global food production. In many farming regions, traditional irrigation methods still depend on manual judgment or fixed scheduling, which often results in excessive watering, uneven moisture distribution, nutrient leaching, and long-term soil degradation. These issues not only waste valuable water resources but also increase operational costs and reduce crop yield. To address these limitations, this project introduces an advanced smart irrigation system that integrates real-time environmental monitoring with a machine learning– driven weather prediction framework to automate and optimize irrigation decisions.

A relay-driven water pump mechanism enables automated control of irrigation hardware, eliminating the need for human supervision. When the system detects adequate soil moisture or forecasts expected rainfall, it postpones or stops irrigation to prevent water wastage. Conversely, when data indicates dry conditions or high evapotranspiration rates, the system activates the pump to maintain optimal soil moisture levels for crop growth. This intelligent decision-making significantly reduces water consumption, improves crop health, and enhances overall farm productivity.

The prototype results show that combining IoT hardware with predictive analytics creates a highly efficient, scalable, and adaptable irrigation method suitable for both small farms and large agricultural operations. By leveraging machine learning models, the system can continuously improve its prediction accuracy over time, making it a robust solution for climate-resilient agriculture. Ultimately, this smart irrigation framework demonstrates how modern sensing technologies and data-driven automation can transform conventional farming practices into more sustainable, resource-efficient, and environmentally friendly systems.

Keywords: Internet of Things (IoT), Wireless Sensor Networks, Machine Learning, Weather Prediction, Smart Agriculture, Precision Farming

Downloads: | DOI: 10.17148/IJARCCE.2026.15129